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Record W4327942887 · doi:10.1183/2312508x.10003722

Food insecurity and respiratory ill health

2023· book-chapter· en· W4327942887 on OpenAlexaff
Elissa M. Abrams

Bibliographic record

VenueEuropean Respiratory Society eBooks · 2023
Typebook-chapter
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFood insecurityEnvironmental healthRespiratory systemMedicineIntensive care medicineFood securityGeographyInternal medicineAgriculture

Abstract

fetched live from OpenAlex

Food insecurity is a significant public health outcome that contributes to the prevalence and severity of chronic respiratory conditions in both childhood and adulthood, including asthma, cystic fibrosis and COPD. Food insecurity influences health outcomes through multiple mechanisms, including poverty, poor dietary diversity and inadequate nutrition. However, it can be difficult to untangle the solitary effects of food insecurity from the broader associated adverse determinants of health. The goal of this chapter is to review the impact of food insecurity on respiratory outcomes in children and adults, as well as outline steps that can be taken to mitigate these effects at local, regional and national levels. Cite as: Abrams EM. Food insecurity and respiratory ill health. In: Sinha IP, Lee A, Katikireddi SV, et al., eds. Inequalities in Respiratory Health (ERS Monograph). Sheffield, European Respiratory Society, 2023; pp. 61–67 [ https://doi.org/10.1183/2312508X.10003722 ].

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0330.011

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.285
GPT teacher head0.407
Teacher spread0.122 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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